Instead of manually designed fusion rules, end-to-end learning methods are proposed to establish the connection between source and fused images directly. Specifically, architectures and loss functions are two challenging stumbling stones for theses methods. Existing methods concentrate on designing architectures based on the current effective practices, rather than considering the particular properties of fusion tasks. For example, dense blocks [1]} and residual blocks [2]} are widely utilized for IVIF.
Besides, diverse loss functions are proposed to enforce different principles for IVIF. Typically, Ma et al. [3]} introduce the dual generative adversarial criterion to push the generated images as similar as the source images. Xu et al. [1]} propose the feature-level
measurement to endow the richness of information into fused images. Tang et al.[2]} uses the max-choosing rule to design loss function, but is limited in extreme scenes (e.g., glare and fog).
